et al., 2013; Suominen et al., 2014). The top soil intake by grazing animals represents the main determinant for the chemical carry-over to food and exposure through food consumption in humans (Brambilla et al., 2011). Beside the chemical contamination, the use of TSIs of anthropogenic and farm origin may cause the spreading of zoonotic and human enteric pathogens in pastures (Yergeau et al., 2016).
Recently, virulence genes associated to important human pathogens, such as Shiga Toxin producing E. coli (STEC) and enteric viruses, were detected by Real Time PCR in TSIs (Tozzoli et al., 2016). A recent study shown that biosolids contain numerous virulence-associated genes related with many pathogens as assessed by metagenomics shotgun sequencing (Yergeau et al., 2016). It is noteworthy that a long term survival (more than 200 days) has been observed for STEC O157 in manure-amended soil (van Hoek et al., 2013). Noro- and Rota-viruses are resistant to wastewater treatment and can persist in the raw sewage and run-off waters from WWTPs as well as in surface waters used in agriculture for several months (López-Gálvez et al., 2016; Zhou et al., 2016). Similarly, parasites as Cryptosporidium parvum and Giardia lamblia have been shown to persist in soils for long times (Helmi et al., 2008). Besides the possibility of transmission of microbial pathogens, transfer of antimicrobial resistance genes from manure to soil bacteria has been described (Heuer and Smalla, 2007; Binh et al., 2008).
Sengeløv et al. (2003) reported that resistance to Tetracycline, Macrolides and Streptomycin was measured for a period of 8 months in soil bacteria obtained from farmland treated with pig manure slurry (Sengeløv et al., 2003). A recent study on the antibiotic resistance in sewage treatment plants has shown that antimicrobial resistance genes can be enriched during sludge treatment process (Bengtsson-Palme et al., 2016).
We used a shotgun metagenomics sequencing approach, to perform the characterization of biological hazards related to the use of TSIs in agriculture in Italy. We selected eight samples including BSO, compost (CO) and mixed compost (MCO) and determined the microbiological profile diversity and to assess the presence of genomics traits related with pathogenic E. coli and antimicrobial resistance (AMR) determi-nants.
2. Materials and methods 2.1. Sampling and samples origin
The specimens analysed in this study were selected among a collection of 24 Top soil improvers samples collected in 2013 from different Italian regions and used in a previous study (Tozzoli et al., 2016). In particular, eight of these have been used for the metage-nomics analysis: four municipal sewage sludges (BSO1, BSO2, BSO3, BSO4), obtained from a tertiary wastewater treatment plants with merged inputs from urban and intensive farmed pigs settlements; three samples of compost, (CO1, CO2 and CO3) derived from green wastes only; and one mixed compost sample (MCO) containing contributions from household wastes, green wastes and urban sewage.
Sampling was performed in accordance with UNI 10802/2004 guideline and the provision of law for the analysis of heavy metals.
Biosolids from Waste Water Treatment Plants were stored in glass jars, in the dark, at– 30 °C, until the analyses were performed.
2.2. Nucleic acid extraction and DNA sequencing
DNA was extracted from 0.25 g of each untreated sample using the Power Soil DNA isolation kit (MO BIO Laboratories inc., Carlsbad, CA, USA) following the manufacturer's instructions.
Sequencing libraries were prepared from 100 ng of the DNA extracted from each sample, using the NEBNext Fast DNA Fragmentation & Lirbary Prep kit (New England BioLabs, New England, USA). In detail, the DNA was enzymatically fragmented to obtain fragments of about 400 bp, through an incubation at 25 °C for
15 min, followed by 10 min at 70 °C. The fragmented DNA was subjected to link with adaptors and size selection of 450 bp fragments by electrophoresis on E-Gel SizeSelect 2% (Invitrogen, Carlsbad, USA) followed by PCR amplification as indicated in the NEBNext Fast DNA Fragmentation & Library Prep kit manual (New England BioLabs, USA). The libraries were amplified individually through emulsion PCR with an Ion OneTouch 2 and sequenced with an Ion Torrent Personal Genome Machine (Life Technologies, 118 Carlsbad, USA), using the 400 bp sequencing protocol. The eight samples were sequenced individually in eight different runs using a 316 V2 chip per run.
2.3. Bioinformatics analysis
The sequences were analysed using the open source webserver for processing metagenomics sequences data MG-RAST to characterize the relative microbiota composition (http://metagenomics.anl.org) (Meyer et al., 2008). In detail, the sequence data werefiltered for the sequences of human origin and analysed for the microbial content and then the resulting reads were compared against M5NR (the M5 non-redundant) protein database, using a maximum e-value of 1e-5, a minimum identity of 60%, and a minimum alignment length of 15 aa for protein and 15 bp for RNA databases (Meyer et al., 2008).
The bioinformatics analyses of the metagenomes were also per-formed using the tools available on the ARIES public webserver (https://w3.iss.it/site/aries/).
In detail, the raw reads were subjected to a quality check and consequent trimming to remove the adaptors and to accept 20 as the lowest Phred value. The identification of the presence of E. coli virulence genes was performed through the pipeline Virulotyper, which employs Bowtie2 algorithm (http://bowtiebio.sourceforge.net/
bowtie2/) (Langmead and Salzberg, 2012) to map the sequencing reads against the E. coli Virulence genes database (Joensen et al., 2014). Virulence genes showing coverage above 1X were considered present in the sample.
Comparative analysis of all samples was performed by using COMMET (COmpare Multiple METagenomes) (Maillet et al., 2014).
This tool allows to observe the homology between the different samples on the basis of all-against-all comparisons of the non-assembled reads (http://colibread.inria.fr/commet/) (Maillet et al., 2014).
Functional metagenomics analysis was performed using the DIAMOND tool (Buchfink et al., 2015). Sequence data in a FASTA format were used to search the COG (Cluster of Orthologous Groups) reference database (Tatusov et al., 1997).
The results of the DIAMOND alignments were analysed and visualized using the MEGAN (MEta Genome ANalyzer) software version 5 (http://ab.inf.unituebingen.de/software/megan5/) (Huson et al., 2007). The SAMfiles produced with the DIAMOND tool were imported into MEGAN 5 together with the FASTAfiles containing the sequence data. The KEGG (Kyoto Encyclopaedia of Genes and Genomes) (Kanehisa and Goto, 2000), COG and SEED subsystems (Overbeek et al., 2005) content was determined using the MEGAN internal Reference sequence maps. MEGAN 5 software was also used to perform the rarefaction analysis (Gotelli and Colwell, 2001) and to calculate the distances between the different samples through the principal coordinate analysis (PCoA) (Smith et al., 2007). For the latter analysis a stress of 0.41 and the Bray Curtis ecological index were used.
3. Results
3.1. Metagenomes quality check
On average, 2,861,113 reads per sample were retained after the quality check and have been used for the subsequent analyses.
The metagenomics datasets are available at the MG-RAST website, under the identification numbers: 4639314.3 (CO1); 4639313.3 (CO2);
4639304.3 (CO3); 4631825.3 (MCO); 4633025.3 (BSO1); 4632987.3
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(BSO2); 4631959.3 (BSO3); 4631832.3 (BSO4).
3.2. Taxonomic profiling and microbial diversity
Rarefaction analysis allows assessing the species richness in a metagenome allowing for meaningful standardization and comparison of datasets, based on the construction of the rarefaction curves (Gotelli et al., 2001). Such an analysis was carried out with the MEGAN 5 software and showed that the profiling of the metagenomes of the compost samples produced a lower number of leaves in the taxonomy tree when compared to the sludges (see Supplementary materials,Fig.
S1), indicating a lower abundance in the taxonomic units in the former samples.
The metagenomes were then analysed at the MG-RAST webserver for the taxonomic analysis. The sequences werefiltered for removing the DNA sequences of human origin and analysed for the microbial content. The MG-RAST result showed an evident predominance of Bacteria vs Archaea, the former representing 92–98% of the total microorganisms’ content and the latter only accounting for 0.5–5.9%.
Approximately, 1.5% of the organisms were from the Eukaryota domain in all the samples, while the remaining comprised DNA viruses and unclassified organisms.
Euryarchaeota and Crenarchaeota constituted most of archaeal taxa in each analysed TSI specimen, without a specific correlation with their source. As for the Eukaryota domain, a possible relation was observed between the presence of members of the Ascomycota phylum and the origin of the TSI samples. As a matter of fact, the compost-based samples CO1, CO2 and CO3 exhibited a high content of Ascomycota, forming the most represented Eukaryotic taxon (80–90%), while their relative abundance was reduced in the MCO (39%) and even lower in all sludges (around 16%).
Actinobacteria (2.6–40%), Proteobacteria (12.8–40%), Bacteroidetes (6.3–65.5%) and Firmicutes (3.6–45.2%) were the most abundant phyla of Bacteria, whereas the phylum Chloroflexi showed a good abundance (14.7%) only in one sample (Table 1).
In addition to Ascomycota, also some orders and classes of the bacterial phyla Proteobacteria, Firmicutes, Chloroflexi and Actinobacteria showed a distribution in the metagenomes markedly associated with the samples’ origin. In particular, the order of Burkholderiales and the classes Bacilli and Thermomicrobia were significantly more represented among the compost samples (P <
0.0001), while the order of Rhodocyclales and the classes of Clostridia and Anaerolineae were more represented in the sludge samples (P < 0.0001) (Fig. 1).
Accordingly, the comparative analyses performed with the PCoA analysis clearly divided the samples into well separate populations (compost vs sludges), based on the composition of the microbiota (Fig. 2B). Similarly, the topology of the dendrogram generated with COMMET showed that the samples grouped under two main branches identifying the sludges population with respect to the compost samples analysed (Fig. 2A).
3.3. Virulence genes associated to pathogenic E. coli
As the TSI samples analysed in this study were already described to
contain genomic traits associated with pathogenic E. coli (Tozzoli et al., 2016), we assayed the capability of the metagenomics approach to identify the presence of such genes in the metagenomes. The mapping of the trimmed raw reads against the E. coli Virulence genes database (Joensen et al., 2014) showed the presence of many virulence genes associated to different diarrheagenic E. coli pathotypes (Table 2). This analysis showed that the metagenomics approach can be as effective as other established technologies, such as the real time PCR, in detecting E. coli virulence genes associated with the different E. coli pathogroups.
As a matter of fact, our analysis allowed us, in most cases, to identify genes associated with the E. coli pathogroups detected in the previous study (Tozzoli et al., 2016) (Table 2). In samples MCO, BSO2, BSO3 and BSO4, the metagenomics approach failed to highlight the presence of Enteroaggregative E. coli (EAEC) gene aggR, whose presence was previously detected by real time PCR (Table 2). In the latter two samples, however, other genes related with EAEC were identified (pic and pet;Table 2). On the other hand, in sample MCO the metage-nomics approach allowed us to identify the presence of the gene ipaH, which was also searched with negative results by real time PCR (Tozzoli et al., 2016). Moreover, through the metagenomics approach, we could observe the presence of genes associated with Enteropathogenic E. coli (EPEC) and Shiga-toxin producing E. coli (STEC) (toxB, katP and tir) in samples CO2 and CO3, which were negative for the presence of the assayed EPEC and STEC-associated genes in the previous real time PCR experiments (Tozzoli et al., 2016) (Table 2).
3.4. Genomic traits related to resistance to compounds
The functional analysis applied to the metagenomes, carried out with both the MG-RAST and the MEGAN5 software, revealed the presence, in these samples, of several genetic traits encoding resistance to compounds including antimicrobials (AM) (Fig. 3). The most represented resistance genes were those encoding the multi-drugs efflux pumps and the resistance to cobalt-zinc-cadmium, the latter accounting for 15–50% of the sequence reads in the different meta-genomes out of the total number of those mapping on the class of resistance to compounds determinants. Among the antibiotics, the most relevant class of AM resistance genes (AMR) observed in all the samples tested was that conferring resistance to Fluoroquinolones, with amounts of mapped reads ranging from 16% to 26% in different metagenomes (Fig. 3). The sequences mapping on the beta-lactamases also had a good relative abundance (Fig. 3). Interestingly, the blaR1 gene, encoding theβ-lactam sensor/signal transducer in methicillin-restistant Staphilococcus aureus (Hao et al., 2012) was abundant in samples CO2 and BSO2, (not shown). Finally, the products of the genes conferring resistance to Streptothricin, Fosfomycin and Vancomycin were also identified, although to a lesser extent (0–1.7%) (Fig. 3).
Finally, traits associated with the resistance to Methicillin in Staphylococci accounted for 2.8–7.2% of the reads (Fig. 3).
4. Discussion and conclusion
The use of TSIs represents a great benefit for the agricultural sector, as they provide valuable carbon sources and nutrients. Moreover, the recycling of the organic matter in form of sludges produced at
waste-Table 1
Relative abundance of the main bacterial phyla in the analysed TSI samples. The values indicate the percentage of reads mapping against specific traits of the considered phyla present on the M5NR database. The values depend on the total of the analysed reads.
CO1 CO2 CO3 MCO BSO1 BSO2 BSO3 BSO4
Actinobacteria 40% 25.70% 11.30% 13.60% 4.20% 5.50% 6.70% 2.60%
Proteobacteria 26% 12.80% 37.80% 30.70% 40% 28.40% 39.60% 17.40%
Bacteroidetes 17% 11.60% 39.50% 37.10% 25% 6.30% 27.40% 65.50%
Firmicutes 6.70% 45.20% 3.60% 4.70% 9.30% 23% 8.80% 3.80%
Chloroflexi 2.40% 0.50% 1.10% 2.60% 3.90% 14.70% 1.90% 1.80%
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water treatment plants is seen as the key to improve soil fertility while decreasing the size of the wastes originating from urban settlements as well as from industrial and zootechnical settings. However, treatment of sludges, including disinfection, is generally not performed (Mezzanotte et al., 2007), thus exposing the crops grown in fields
where sludge-based TSIs are used to potential risks of chemical, pharmacological, and microbial contamination of the food chain (Saveyn and Eder, 2014). In Europe a harmonized legislative frame-work on the subject is not in place. Therefore, the regulations vary considerably from country to country. The European Union is inves-Fig. 1. Distribution of the OTUs in the metagenomes significantly associated with the samples’ origin. The scale on the y axis refers to the percent of the reads mapping on the specific OTU. For all the OTUs shown the difference observed was statistically significant with a P value < 0.0001.
Fig. 2. Panel A: Dendrogram showing the distribution of the samples according to their origin obtained with the COMMET tool. Panel B: Principal Coordinate Analysis (PCoA), obtained by using MEGAN5 software. The localization in the graph of each sample is represented by a coloured circle and the distances between the circles reflect the original distance between the samples, calculated using the BrayCurtis ecological index. (For interpretation of the references to color in thisfigure legend, the reader is referred to the web version of this article.)
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tigating the definition of End of Waste criteria to aid the decision on whether certain sludges can be used as fertilizers and cease to be classified as waste (ISWA, 2013). In some European countries the bio-wastes used as TSIs are not allowed to contain sludge, while in others it can be used only after composting and it must fall within the scope of the End of Waste criteria for biodegradable waste, according to Article 6 of the Waste Framework Directive (2008/98/EC, Dir.).
In Italy, criteria related to the sewage sludge use in agriculture have been laid down in the Regulation 99 (99/1992, D.lgs), representing the adoption of the Sewage Sludge Directive 86/278/EEC (86/278/EEC, Dir.). The Directive defines some chemical, physical and biological parameters that TSIs must comply with, such as the absence of Salmonella and limits for E. coli (99/1992, D.lgs). Additionally, it states that the use of untreated sludge (e.g. by heat treatment and/or long-term storage) on agricultural land is not allowed (99/1992, D.lgs) and that these should be labelled differently from those from green and/or household wastes (10/07/2013, Dm).
In a previous study, we have described the presence of virulence genes of pathogenic E. coli and enteric viruses in TSIs in Italy (Tozzoli et al., 2016) and came to the conclusion that a comprehensive risk assessment is needed to come to a sound scientific basis for developing regulations.
In the present study we aimed at using metagenomics as a tool to perform a characterization of the hazards related with the TSIs, not biased towards the search of one specific threat. We have used the shotgun metagenomics to identify the presence of pathogenic E. coli associated genes as well as of determinants associated with resistance to compounds. Moreover, we performed the microbiological profiling
of the TSIs in order to determine if the composition of the microbiota may be used to trace the origin of the matter the TSIs are made with.
The search for the virulence genes in the TSI samples through metagenomics proved effective in detecting evidence of the presence of pathogenic E. coli. The samples analysed in this study had been previously shown to contain genes associated with different E. coli pathogroups by means of a real time PCR screening (Tozzoli et al., 2016). The comparison of the metagenomics results with those obtained in the previous study indicated the former approach as a promising strategy for the assessment of the presence of genomic traits associated with any pathogen in a complex matrix, such as the TSIs, with the only limitation of the availability of comprehensive databases of markers for the pathogens of interest. As a matter of fact, our results showed a good correlation between the virulence genes content of the TSIs identified in this study and the previously published analysis (Tozzoli et al., 2016) (Table 2). In some cases, as for the samples MCO, BSO2, the metagenomics approach failed to identify genes associated with Enteroaggregative E. coli (EAEC). However, in samples BSO3 and BSO4, the evidence of EAEC was provided by the presence of other genes associated with this E. coli pathogroup, but different from those used in the real time PCR experiments described previously. This discrepancy is not unexpected as the real time PCR is based on the amplification of specific DNA sequence, which can be present in the sample in a very low abundance and below the sensitivity limit of a non-target approach as the metagenomics. On the other hand, this approach provided evidence of genes characteristic of other pathogenic E. coli, which were not detected with the PCR-based approach (Table 2). While the real time PCR approach may be more sensitive, Table 2
Comparison of diarrheagenic E. coli virulence genes emerged by applying metagenomics approach (this study) and by real time PCR (Tozzoli et al., 2016). In bracket are reported the samples codes used inTozzoli et al., 2016for cross reference.
CO1 (13–0487) CO2 (13–0485) CO3 (13–0483) MCO (13–0486) BSO1 (13–0493) BSO2 (13–0489) BSO3 (13–0492) BSO4 (13–0490)
This study
Tozzoli et al.
This study
Tozzoli et al.
This study
Tozzoli et al.
This study
Tozzoli et al.
This study
Tozzoli et al.
This study
Tozzoli et al.
This study
Tozzoli et al.
This study
Tozzoli et al.
espI katP espC aggR katP lt iroN aggR katP aggR astA; bfpA aggR
iha nleB fedF prfB prfB stx2 nleB eae cba; ccl eae
prfB pet ipaH tir tir pet ipaH cdtB; cfaC stx2
tir prfB prfB vat toxB prfB stx2 cif; cma
toxB tir tir tir cnf1; eae
eatA; efa1 espA; espB espC; espF espI; espJ espP; etpD fim41A;
ehxA hlyE; ihaA ireA; iroN ipaH; iss K88; katP nleB; nleC pet; pic picU; prfB senB;sepA stx1Ac;
stx1Bc stx1Ad;
stx1Bd stx1Aa stx2Aa;
stx2Ad stx2Ae;
stx2Af stx2Ag subA; saa tccP; tir toxB; tsh virF
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due to the exponential amplification of the targets, the metagenomics approach may complement the lower technical sensitivity with the simultaneous search for more determinants associated with the same pathogroup, as in the case of samples BSO3 and BSO4. It has to be stressed that the focus on the identification of pathogenic E. coli was chosen as a good database is available containing all the known sequences of the virulence genes and their alleles (Joensen et al., 2014). While the existence of accurately curated databases with the genetic markers for other pathogenic agents may not be available yet, this is afield of development where many research groups are currently operating. Moreover, also in the lack of specific databases, there is already the possibility to screen metagenomes for several pathogens using the functional metagenomics approach based on the detection of the virulence-associated proteins present in the large non-redundant proteins (Pruitt et al., 2005) or the seed (Overbeek et al., 2005) databases. Such an approach was already proved functional in detect-ing virulence genes of multiple pathogens in samples from wastewater treatment facilities in Canada (Yergeau et al., 2016).
The metagenomics approach allowed us to investigate the presence, in TSIs, of determinants conferring resistance to compounds including antimicrobials (AM). The most abundant factors identified were the multidrug-resistance efflux pumps (Fig. 3), which can confer resistance to clinically relevant antibiotics. Those pumps transport several sub-stances produced by the host, improving resistance to host defence molecules, and can transport antibiotics of different chemical classes, conferring decreased susceptibility to antimicrobial (Piddock, 2006).
The analysis also showed that genes conferring resistance to specific classes of antibiotics, such as the Fluoroquinolones and the Beta-lactams, as well as Streptothricin, Fosfomycin, Vancomycin and Methicillin, were also represented, suggesting that, following the use of these TSIs, they could be transferred to microbial soil communities, contributing to the overall spread of antibiotic resistance. Our results
are in line with those reported in a recent study, describing the antimicrobial resistance genes in sewage from wastewater treatment plants in Sweden (Bengtsson-Palme et al., 2016). One of the most abundant classes of genes governing the resistance to compounds identified in the metagenomes in this study was that related with the resistance to heavy metals (Fig. 3). Thisfinding is interesting, as it has been proposed that the contamination of the environment with such metals may play a role in the maintenance of AMR genes in the microbial communities in the soil (Alonso et al., 2001; Baker-Austin et al., 2006). In another recent study, Di Cesare and colleagues also reported the co-occurrence of heavy metals and antibiotic resistance genes within the resident bacterial communities in urban WWTPs, in the different steps of the treatment process. Moreover, these authors could successfully isolate living microorganisms carrying such resis-tance genes (Di Cesare et al., 2016). These observations confirm that the sludges in Italy may be a source of antimicrobial resistance genes and that their release in the environment through the land application of TSIs may pose a risk for the diffusion of AMR to the soil and the crop bacterial communities.
The taxonomic profiling of the TSI highlighted differences in the composition of the microbiota between the analysed samples composed of sludges, compost and mixed compost, showing a higher richness in taxonomic units in the former sample types. This finding is not surprising as the sludges samples were from wastewater treatment plants collecting inputs from urban and livestock wastes.
A deeper analysis showed that some classes of the Actinobacteria phylum prevailed in the compost samples (CO1 and CO2, and to a lesser extent MCO and CO3), while showed a much lower representa-tion in the four analysed sludge-based BSO. This phylum shows a wide diffusion in the aquatic and terrestrial ecosystem (Alvarez et al., 2016) and its abundance in the compost-based samples analysed here can be explained by the inclusion of the matter from the pruning of the trees of Fig. 3. Distribution of the reads mapping on specific determinants conferring resistance to compounds. The values on the Y axis correspond to the percentage of the reads mapping on specific determinants out of the total number of reads mapping on this specific class of determinants.
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the urban areas in the composting process. Similarly, in the Eukaryota domain, the three compost samples exhibited a high content of Ascomycota phylum, which appeared reduced in the MCO and even lower in all the sludges (Fig. 1). Beside the implication of the association between specific operational taxonomic units (OTU) and the source of the samples, the taxonomy profiles of the TSIs obtained in this study allowed us to group the samples based on their origin. The PCoA analysis done using the data on the taxonomy confirmed the ability of the metagenomics profiling to identify separate groups of samples based on their taxonomic composition (Fig. 2B). In particular, the sludge-based BSO samples all grouped on the right of the chart, while the compost-based CO samples placed on the left. The mixed compost MCO sample was represented by the closest circle to the line separating the two groups (Fig. 2B). This topology reflects the declared presence of sludge in the mixed compost, which is allowed to contain up to 30% of this matter according to the Italian legislation. Similarly, the analysis performed using the COMMET software also showed a net separation between the group of compost and the sludge samples (Fig. 2A). This observation is interesting as the COMMET tool performs a comparison between the sequence content of the metagenomes and does not perform any annotation and OTU assignment. This analysis could allow identifying the origin of a TSI in a quick and easy way and could enable the risk evaluators to promptly respond to regulatory requirements.
The results of this study indicate that metagenomics is efficient to detect genomic traits associated with pathogens and antimicrobial resistance when comprehensive databases are available. Moreover, the taxonomic profiling of TSIs could allow to identify the source of TSI samples with unknown origin, making metagenomics a promising tool for the traceability of TSI in presence of a regulatory framework establishing limits in terms of presence of sludges in compost used to amend agricultural soils. This could be particularly useful to improve the development of a harmonized legislation stating that top soil improvers not falling within the scope of the End of Waste criteria can’t be used as fertilizers, as considered at highest risk for human and animal health.
Funding sources supporting the paper
The Italian Ministry of Health grant no. RF-2009-1534860, “ENVI-FOOD” project, is acknowledged.
Appendix A. Supplementary material
Supplementary material associated with this article can be found in the online version atdoi:10.1016/j.envres.2017.02.004.
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